Data Wrangling
library(httr)
library(curl)
library(ggplot2)
library(plotly)
library(plyr)
library(stringr)
library(sqldf)
library(dplyr)
library(tidytext)
library(tidyverse)
library(gganimate)
library(readxl)
library(rvest)
library(lubridate)
library(stringi)
library(rvest)
library(jsonlite)
library(choroplethr)
library(choroplethrMaps)
library(rlang)
library(topicmodels)The usecase numbers are aligned with the slides presentation.
library(readxl)
library(dplyr)
kaggle_data <-readxl::read_excel("athlete_events.xlsx")
#Usecase 1 : Growth in the number of sports and events in Summer & Winter Games – Kaggle Data range from 1896 to 2016
Usecase_1 <- kaggle_data %>% distinct(Year,Season,Sport)
Usecase_1 <- Usecase_1 %>% select(Year,Season) %>% dplyr::arrange(Year,Season) %>% dplyr::count(Year,Season) %>% dplyr::rename(freq=n)
plot <- plot_ly(data = Usecase_1, x = ~Year, y = ~freq, type = 'scatter',
marker = list(size = 10,
color = 'green',
line = list(color = 'orange',
width = 2)))
plot <- plot_ly() %>%
add_trace( data = dplyr::filter(Usecase_1,Season == "Summer"),
x = ~Year, y = ~freq, type = 'scatter',
name = 'Summer Sports',
marker = list(size = 10,
color = 'red',
line = list(color = 'green',
width = 2))) %>%
add_trace( data = dplyr::filter(Usecase_1,Season == "Winter"),
x = ~Year, y = ~freq, type = 'scatter',
name = 'Winert Sports',
marker = list(size = 10,
color = 'yellow',
line = list(color = 'green',
width = 2)))
plot <- plot %>% layout(title = 'Number of Summer Vs Winter sport Olympics since 1920',
plot_bgcolor = "#e5ecf6",scale_fill_brewer(palette = "RdYlGn"),
yaxis = list(zeroline = FALSE, title = "Year of Olympics"),
xaxis = list(zeroline = FALSE, title = "Number of Sports"),
legend = list(title=list(text='Legend Title')))
ggplotly(plot)#From the plot we can see that the growth in the number of winter sports is very less when compared to the summer sports, and this is due to weather, ease of hosting and the audience turn out not being on the side of winter sports#2. Gender Participation in Olympics – Kaggle Data range from 1896 to 2016
data(df_pop_country)
kaggle_data <- readxl::read_excel("athlete_events.xlsx")
Usecase_2 <- kaggle_data %>% select(Team,Sex)
Usecase_2 %>% filter(Team == "united states of america")## # A tibble: 0 × 2
## # … with 2 variables: Team <chr>, Sex <chr>Usecase_2 <- Usecase_2 %>%
mutate(Team = str_extract(Team,"[a-zA-Z' ']+")) %>%
mutate(Team = str_to_lower(Team,"[a-zA-Z]+"))
Usecase_2$Team <- str_replace(Usecase_2$Team, "united states","united states of america")
Usecase_2 %>% filter(Team == "united states of america")## # A tibble: 18,475 × 2
## Team Sex
## <chr> <chr>
## 1 united states of america M
## 2 united states of america M
## 3 united states of america M
## 4 united states of america M
## 5 united states of america M
## 6 united states of america M
## 7 united states of america M
## 8 united states of america M
## 9 united states of america M
## 10 united states of america M
## # … with 18,465 more rows#2. Nations (in terms of numbers) participating in Olympics to identify gender distribution
result_df <- sqldf("select Team as region, Sex, count(Sex) as value from Usecase_2 Group by Team,Sex")
Usecase_2 <- as_tibble(result_df)
Usecase_2_M <- Usecase_2 %>% filter(Sex == 'M') %>% select(1,3)
Usecase_2_M <- inner_join(df_pop_country,Usecase_2_M, by = 'region') %>% select(1,3)
Usecase_2_M <- as_tibble(Usecase_2_M)
Usecase_2_M <- dplyr::rename(Usecase_2_M,value = value.y)
head(Usecase_2_M)## # A tibble: 6 × 2
## region value
## <chr> <int>
## 1 united arab emirates 137
## 2 afghanistan 121
## 3 albania 43
## 4 armenia 183
## 5 angola 138
## 6 argentina 2590Usecase_2_M %>% filter(region == "china")## # A tibble: 1 × 2
## region value
## <chr> <int>
## 1 china 2371country_choropleth(Usecase_2_M)
Usecase_2_F <- Usecase_2 %>% filter(Sex == 'F') %>% select(1,3)
Usecase_2_F <- inner_join(df_pop_country,Usecase_2_F, by = 'region') %>% select(1,3)
Usecase_2_F <- as_tibble(Usecase_2_F)
Usecase_2_F <- dplyr::rename(Usecase_2_F,value = value.y)
head(Usecase_2_F)## # A tibble: 6 × 2
## region value
## <chr> <int>
## 1 united arab emirates 7
## 2 afghanistan 5
## 3 albania 27
## 4 armenia 38
## 5 angola 129
## 6 argentina 643Usecase_2_F %>% filter(region == "china")## # A tibble: 1 × 2
## region value
## <chr> <int>
## 1 china 2770country_choropleth(Usecase_2_F)
# The Chloropleth plots of Men and women participation show differences in the scale due to
# women entering Olympics later than men. We can also see that there are cases where the men participation is much higher than women participation (like Denmark) or where women participation better than men's participation (like China)## usecase3 : Medal Summary by Nations
url <- "https://en.wikipedia.org/wiki/Swimming_at_the_Summer_Olympics"
extractdata <- url %>% read_html() %>% html_table(fill = TRUE)
Swimming_Medal_Summary_byNations <- extractdata[[11]] %>% select(2,6)
head(Swimming_Medal_Summary_byNations)## # A tibble: 6 × 2
## Nation Total
## <chr> <int>
## 1 United States (USA) 579
## 2 Australia (AUS) 213
## 3 East Germany (GDR) 92
## 4 Hungary (HUN) 76
## 5 Japan (JPN) 83
## 6 Netherlands (NED) 62url <- "https://en.wikipedia.org/wiki/List_of_Olympic_medalists_in_speed_skating"
extract <- url %>% read_html() %>% html_table(fill = TRUE)
Skating_medal_summary <- extract[[34]] %>% select(Country,Total)
head(Skating_medal_summary)## # A tibble: 6 × 2
## Country Total
## <chr> <chr>
## 1 Austria (AUT) 6
## 2 Belarus 1
## 3 Belgium 3
## 4 Canada (CAN) 42
## 5 China 9
## 6 Czech Republic 7Swimming_Medal_Summary <- Swimming_Medal_Summary_byNations
Swimming_Medal_Summary <- dplyr::rename(Swimming_Medal_Summary,Country = Nation)
Swimming_Medal_Summary <- Swimming_Medal_Summary %>% arrange(Total) %>% head(-1)
Swimming_Medal_Summary <- Swimming_Medal_Summary %>%
mutate(Country = str_extract(Country, "[a-zA-Z' ']+"))
Swimming_Medal_Summary <- Swimming_Medal_Summary %>% mutate(Sport = "Swimming")
head(Swimming_Medal_Summary)## # A tibble: 6 × 3
## Country Total Sport
## <chr> <int> <chr>
## 1 Kazakhstan 1 Swimming
## 2 Lithuania 1 Swimming
## 3 Singapore 1 Swimming
## 4 Croatia 1 Swimming
## 5 Serbia 1 Swimming
## 6 Slovenia 1 SwimmingSkating_medal_summary$Total <- as.integer(Skating_medal_summary$Total)
Skating_medal_summary <- Skating_medal_summary %>% na.omit()
Skating_medal_summary <- Skating_medal_summary %>%
mutate(Country = str_extract(Country, "[a-zA-Z' ']+"))
Skating_medal_summary <- Skating_medal_summary %>% mutate(Sport = "Skating")
tail(Skating_medal_summary) ## # A tibble: 6 × 3
## Country Total Sport
## <chr> <int> <chr>
## 1 Russia 13 Skating
## 2 Olympic Athletes from Russia 3 Skating
## 3 South Korea 20 Skating
## 4 Soviet Union 60 Skating
## 5 Sweden 18 Skating
## 6 United States 71 SkatingExtract <- bind_rows(Swimming_Medal_Summary,Skating_medal_summary)
Extract <- Extract %>% arrange(Country,Sport)
Extract %>%
ggplot(aes( x = reorder(Country,Total,sum), y= Total, fill=Sport,
label = Total)) +
geom_bar(stat="identity")+ xlab(NULL) +coord_flip() +
geom_text(size = 3, position = position_stack(vjust = 0.5)) +
xlab("Country") + ylab("Total number of Medals")
#The graph gives visualization of the total medals won by countries across the world in skating and swimming and at the same time the graph
#Observation from the graph: USA is obviously standing out with the medal count.
#A section of nations have been performing well consistently where as another section of nations have been performing not so very well at all
#Through the same graph we can see the overall performance as well the as the performance
# for each sport at the same time##4:Male vs Female participation over time in Olympics. – Input Kaggle – Year, Gender
rm(Medal_Summary_byGender)
library(readxl)
Medal_Summary_byGender <- readxl::read_excel("athlete_events.xlsx")
Medal_Summary_byGender <- Medal_Summary_byGender %>% filter(Sport != "Art Competitions")
## Year" for summer and winter
original <- c(1994,1998,2002,2006,2010,2014)
new <- c(1996,2000,2004,2008,2012,2016,2020)
for (i in 1:length(original)) {
Medal_Summary_byGender$Year <- gsub(original[i], new[i], Medal_Summary_byGender$Year)
}
Medal_Summary_byGender$Year <- as.integer(Medal_Summary_byGender$Year)
## Table counting number of athletes by Year and Sex
count_byGender <- Medal_Summary_byGender %>% group_by(Year, Sex) %>% select(Year,Sex) %>% count() %>% dplyr::rename(Count=n)
count_byGender$Year <- as.integer(count_byGender$Year)
count_byGender <- count_byGender %>% dplyr::arrange(desc(Count))
## Plot number of male/female athletes vs time
Plot_gender_distribution <- ggplot(count_byGender, aes(x=Year, y=Count, group=(Sex), color=Sex)) +
geom_point(size=3) +
geom_line() +
scale_color_manual(values=c("darkgreen","Orange")) +
labs(x = "Year", y = "Olympians Count",title = "Number of Male and Female Olympians Participation over time in Years")
Plot_gender_distribution
##here is a clear increase in the inclusion of the female after 1950 in competitions, however, the male participation is erratic.
##Zero Female Participation in 7 games. ex: Jeu De Paume,Lacrosse,Military Ski Patrol,Polo,Racquets,Roque,Tug-Of-War
##Zero Male participation in Rhythmic Gymnastics.# Male vs Female participation over time in Olympics Medal summary
rm(Medal_Summary_bynations)
library(readxl)
Medal_Summary_bynations<- readxl::read_excel("athlete_events.xlsx")
Medal_Summary_bynations <- Medal_Summary_bynations %>% filter(Medal != "NA") %>% na.omit()
Medal_Summary_bynations <- Medal_Summary_bynations %>% filter(Sport != "Art Competitions") %>%
dplyr::rename(region = Team) %>%
group_by(NOC, Medal, Sport, Year,Sex,region) %>% summarise(isMedal=1) %>%
dplyr::arrange(Medal,Year)
Medal_Summary_bynations <- Medal_Summary_bynations %>% group_by(Medal,Sex,Year) %>%
summarise(Count= sum(isMedal)) %>% dplyr::arrange(Year)
# TOTAL MEDAL COUNT for Male by Year over time
Medal_Summary_bynations_Male <- Medal_Summary_bynations %>% filter(Sex == "M") %>%
group_by(Medal,Year,Sex) %>% summarise(Total=sum(Count)) %>% dplyr::arrange(desc(Medal))
## Plot
plot_Male <- ggplot(data=Medal_Summary_bynations_Male, aes(Year,Total , color=Medal)) +
geom_point() + ggtitle("Number of Male Olympians Participation over time in Years") +
geom_smooth()
plot_Male
# TOTAL MEDAL COUNT for Female by Year over time
Medal_Summary_bynations_Female <- Medal_Summary_bynations %>% filter(Sex == "F") %>%
group_by(Medal,Year,Sex) %>% summarise(Total=sum(Count)) %>% dplyr::arrange(desc(Medal))
plot_Female <- ggplot(data= Medal_Summary_bynations_Female, aes(Year,Total,color=Medal)) +
geom_point() + ggtitle("Number of female Olympians Participation over time in Years") +
geom_smooth()
plot_Female
## Female participations started increasing after 1976 with 12 teams bringing in at least 25% women.
##In 1992, the winter sports are separated which skews the trends for both.
##Medal Dominations by country: American women : 2016
##East German & Soviet Women : 1976
##Germany women : 1936##6. Locations of Olympics by year choloropleth - Male Vs Female
rm(Olympic_Games_host_cities)
rm(Olympic_Games_host_cities_1)
Olympic_Games_host_cities <- "https://en.wikipedia.org/wiki/List_of_Olympic_Games_host_cities"
Olympic_Games_host_cities <- Olympic_Games_host_cities[1] %>% read_html() %>% html_table(fill = TRUE)
Olympic_Games_host_cities_1 <- Olympic_Games_host_cities[[2]] %>% select(2,3,4,6,7)
Olympic_Games_host_cities_df <- as.data.frame(Olympic_Games_host_cities_1)
Olympic_Games_host_cities_df_summer <- Olympic_Games_host_cities_df %>%
arrange(Year)
Olympic_Games_host_cities_df_summer <- Olympic_Games_host_cities_df_summer %>% dplyr::rename(region=Country)
Olympic_Games_host_cities_df_summer$region = tolower(Olympic_Games_host_cities_df_summer$region )
Olympic_Games_host_cities_df_summer[Olympic_Games_host_cities_df_summer$region=="soviet union[h]", "region"] <- "russia"
Olympic_Games_host_cities_df_summer[Olympic_Games_host_cities_df_summer$region=="russia[h]", "region"] <- "russia"
Olympic_Games_host_cities_df_summer <- Olympic_Games_host_cities_df_summer %>%
dplyr::rename(value=Year)
Olympic_Games_host_cities_df_summer <- Olympic_Games_host_cities_df_summer %>% select(2,3)
Olympic_Games_host_cities_df_summer <- Olympic_Games_host_cities_df_summer %>% arrange(region,value) %>%
dplyr::count(region) %>% dplyr::rename(value = n)
Olympic_Games_host_cities_df_summer$region <- str_replace(Olympic_Games_host_cities_df_summer$region, "united states","united states of america")
country_choropleth(Olympic_Games_host_cities_df_summer,num_colors=5 ) +
scale_fill_brewer(palette="YlOrRd") +
labs(title = "Countries hosted Olympics",
subtitle = "Year range",
caption = "source: https://en.wikipedia.org/wiki/List_of_Olympic_Games_host_cities")
My first task was to extract the Kaggle data and filter for my sport:Athlectics (Track & Field). The Kaggle table provides important information about the contestants and the event, but with one glaring ommission: Kaggle does not include the performance (time, height, score, etc.) of the actual event. For this reason, additional data had to be scraped from Wikipedia.
kaggle_athletics <- read_excel("athlete_events.xlsx")%>%
filter(Sport == "Athletics")
head(kaggle_athletics)## # A tibble: 6 × 15
## ID Name Sex Age Height Weight Team NOC Games Year Season City
## <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 8 "Corneli… F 18 168 NA Neth… NED 1932… 1932 Summer Los …
## 2 8 "Corneli… F 18 168 NA Neth… NED 1932… 1932 Summer Los …
## 3 18 "Timo An… M 31 189 130 Finl… FIN 2000… 2000 Summer Sydn…
## 4 31 "Evald r… M 24 174 70 Esto… EST 1936… 1936 Summer Berl…
## 5 32 "Olav Au… M 23 NA NA Norw… NOR 1912… 1912 Summer Stoc…
## 6 34 "Jamale … M 30 187 76 Fran… FRA 2012… 2012 Summer Lond…
## # … with 3 more variables: Sport <chr>, Event <chr>, Medal <chr>Wikipedia has separate sites for each Olympic year with separate tables, which varied by year, for male and female performance. To join the Wikipedia data with Kaggle, we first had to scrape it from the Wikipedia site, rename the columns, clean the event fields in both the Kaggle and Wikipedia files, so that they would match.
To do this, We created the “olympic_join” function function that accepted year, Wikipedia URL, table number (varied by year) and gender. The join resulted in two combined files (“M” and “F”) for Gold, Silver and Bronze for each Olympic year.
olympic_join = function(func_year, func_url,table_num,gender) {
#Get the Kaggle files and remove the prefixes "Athletics Men's" and Athletics Women's from the event field.
kaggle_athletics_year <- kaggle_athletics%>%
filter(Sex == gender, Year == func_year) %>% arrange(Year) %>%
mutate(Event = trimws(str_replace_all(Event, "Athletics Men's", ""))) %>%
mutate(Event = trimws(str_replace_all(Event, "Athletics Women's", "")))
#Scrape the data from Wikipedia given the functional year.
athletics_year <- func_url%>%
read_html() %>%
html_nodes("table")%>%
html_table(fill=TRUE)
#rename columns to guaranty first column is always "Event" even if column title changes for each Olympic year, and each of the pairs of name and performance for each medal is titled "Name" and "Time."
wiki_year <- athletics_year[[table_num]]
colnames(wiki_year)[1] <- "Event"
colnames(wiki_year)[2] <- "Name"
colnames(wiki_year)[3] <- "Time"
colnames(wiki_year)[4] <- "Name"
colnames(wiki_year)[5] <- "Time"
colnames(wiki_year)[6] <- "Name"
colnames(wiki_year)[7] <- "Time"
#Clean field on Wiki, removing "details". Join with Kaggle for gold medalists (rank =1).
wiki_year_g<- wiki_year %>%
select (1:3)%>%
mutate(Event = str_replace_all(Event,"details",""))
wiki_year_g$Rank <- "1"
#Kaggle athletic data and wikipedia data joined for year Gold.
kaggle_athletics_year_g <- kaggle_athletics_year%>%
filter(Year == func_year, Medal == "Gold", Event == "Marathon" | Event == "100 metres" | Event == "800 metres")
final_year_g <- left_join(kaggle_athletics_year_g,wiki_year_g,by = "Event")
#Repeat year for Silver
wiki_year_s<- wiki_year %>%
select (1, 4:5)%>%
mutate(Event = str_replace_all(Event,"details",""))
wiki_year_s$Rank <- "2"
#Kaggle athletic data and wikipedia data joined for year and silver medalists.
kaggle_athletics_year_s <- kaggle_athletics%>%
filter(Year == func_year, Medal == "Silver", Event == "Marathon" | Event == "100 metres" | Event == "800 metres")
final_year_s <- left_join(kaggle_athletics_year_s,wiki_year_s,by = "Event")
#Do the same for Bronze
wiki_year_b<- wiki_year %>%
select (1, 6:7)%>%
mutate(Event = str_replace_all(Event,"details",""))
wiki_year_b$Rank <- "3"
#Kaggle athletic data and wikipedia data joined for year and bronze medalists.
kaggle_athletics_year_b <- kaggle_athletics%>%
filter(Year == func_year, Medal == "Bronze", Event == "Marathon" | Event == "100 metres" | Event == "800 metres")
final_year_b <- left_join(kaggle_athletics_year_b,wiki_year_b,by = "Event")
#Consolidate gold, silver and bronze for year
final_year <- final_year_g %>%
rbind.data.frame(final_year_s) %>%
rbind.data.frame(final_year_b)
# select(1:7, 10:13, 15, 16, 19)
final_year
}Begin calling the function olympic_join for each of the Olympics by year, starting in 2016.
result_m <- olympic_join("2016","https://en.wikipedia.org/wiki/Athletics_at_the_2016_Summer_Olympics", 6, "M")
result_w <- olympic_join("2016","https://en.wikipedia.org/wiki/Athletics_at_the_2016_Summer_Olympics", 7, "F")The rows of each subsequent years are appended to 2016, separated by sex.
result_new <- olympic_join("2012", "https://en.wikipedia.org/wiki/Athletics_at_the_2012_Summer_Olympics",7, "M")
result_m <- result_m %>%
rbind.data.frame(result_new)
result_new <- olympic_join("2012", "https://en.wikipedia.org/wiki/Athletics_at_the_2012_Summer_Olympics",8, "F")
result_w <- result_w %>%
rbind.data.frame(result_new)result_new <- olympic_join("2008", "https://en.wikipedia.org/wiki/Athletics_at_the_2008_Summer_Olympics",4, "M")
result_m <- result_m %>%
rbind.data.frame(result_new)
result_new <- olympic_join("2008", "https://en.wikipedia.org/wiki/Athletics_at_the_2012_Summer_Olympics",5, "F")
result_w <- result_w %>%
rbind.data.frame(result_new)result_new <- olympic_join("2004", "https://en.wikipedia.org/wiki/Athletics_at_the_2004_Summer_Olympics",3, "M")
result_m <- result_m %>%
rbind.data.frame(result_new)
result_new <- olympic_join("2004", "https://en.wikipedia.org/wiki/Athletics_at_the_2004_Summer_Olympics",4, "F")
result_w <- result_w %>%
rbind.data.frame(result_new)result_new <- olympic_join("2000", "https://en.wikipedia.org/wiki/Athletics_at_the_2000_Summer_Olympics",3, "M")
result_m <- result_m %>%
rbind.data.frame(result_new)
result_new <- olympic_join("2000", "https://en.wikipedia.org/wiki/Athletics_at_the_2000_Summer_Olympics",4, "F")
result_w <- result_w %>%
rbind.data.frame(result_new)result_new <- olympic_join("1996", "https://en.wikipedia.org/wiki/Athletics_at_the_1996_Summer_Olympics",2, "M")
result_m <- result_m %>%
rbind.data.frame(result_new)
result_new <- olympic_join("1996", "https://en.wikipedia.org/wiki/Athletics_at_the_1996_Summer_Olympics",3, "F")
result_w <- result_w %>%
rbind.data.frame(result_new)result_new <- olympic_join("1992", "https://en.wikipedia.org/wiki/Athletics_at_the_1992_Summer_Olympics",2, "M")
result_m <- result_m %>%
rbind.data.frame(result_new)
result_new <- olympic_join("1992", "https://en.wikipedia.org/wiki/Athletics_at_the_1992_Summer_Olympics",3, "F")
result_w <- result_w %>%
rbind.data.frame(result_new)
head(result_w)## # A tibble: 6 × 18
## ID Name.x Sex Age Height Weight Team NOC Games Year Season City
## <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 108532 Mokgadi… F 25 178 70 Sout… RSA 2016… 2016 Summer Rio …
## 2 116424 Jemima … F 31 160 45 Kenya KEN 2016… 2016 Summer Rio …
## 3 119903 Elaine … F 24 167 57 Jama… JAM 2016… 2016 Summer Rio …
## 4 36870 Shelly-… F 25 160 57 Jama… JAM 2012… 2012 Summer Lond…
## 5 39396 Erba Ti… F 24 165 48 Ethi… ETH 2012… 2012 Summer Lond…
## 6 106272 Mariya … F 26 172 60 Russ… RUS 2012… 2012 Summer Lond…
## # … with 6 more variables: Sport <chr>, Event <chr>, Medal <chr>, Name.y <chr>,
## # Time <chr>, Rank <chr>result_new <- olympic_join("1988", "https://en.wikipedia.org/wiki/Athletics_at_the_1988_Summer_Olympics",2, "M")
result_m <- result_m %>%
rbind.data.frame(result_new)
result_new <- olympic_join("1988", "https://en.wikipedia.org/wiki/Athletics_at_the_1988_Summer_Olympics",3, "F")
result_w <- result_w %>%
rbind.data.frame(result_new)result_new <- olympic_join("1984", "https://en.wikipedia.org/wiki/Athletics_at_the_1984_Summer_Olympics",3, "M")
result_m <- result_m %>%
rbind.data.frame(result_new)
result_new <- olympic_join("1984", "https://en.wikipedia.org/wiki/Athletics_at_the_1984_Summer_Olympics",4, "F")
result_w <- result_w %>%
rbind.data.frame(result_new)result_new <- olympic_join("1980", "https://en.wikipedia.org/wiki/Athletics_at_the_1980_Summer_Olympics",4, "M")
result_m <- result_m %>%
rbind.data.frame(result_new)
result_new <- olympic_join("1980", "https://en.wikipedia.org/wiki/Athletics_at_the_1980_Summer_Olympics",5, "F")
result_w <- result_w %>%
rbind.data.frame(result_new)result_new <- olympic_join("1976", "https://en.wikipedia.org/wiki/Athletics_at_the_1976_Summer_Olympics",1, "M")
result_m <- result_m %>%
rbind.data.frame(result_new)
result_new <- olympic_join("1976", "https://en.wikipedia.org/wiki/Athletics_at_the_1976_Summer_Olympics",2, "F")
result_w <- result_w %>%
rbind.data.frame(result_new)result_new <- olympic_join("1972", "https://en.wikipedia.org/wiki/Athletics_at_the_1972_Summer_Olympics",1, "M")
result_m <- result_m %>%
rbind.data.frame(result_new)
result_new <- olympic_join("1972", "https://en.wikipedia.org/wiki/Athletics_at_the_1972_Summer_Olympics",2, "F")
result_w <- result_w %>%
rbind.data.frame(result_new)result_new <- olympic_join("1968", "https://en.wikipedia.org/wiki/Athletics_at_the_1968_Summer_Olympics",2, "M")
result_m <- result_m %>%
rbind.data.frame(result_new)
result_new <- olympic_join("1968", "https://en.wikipedia.org/wiki/Athletics_at_the_1968_Summer_Olympics",3, "F")
result_w <- result_w %>%
rbind.data.frame(result_new)result_new <- olympic_join("1964", "https://en.wikipedia.org/wiki/Athletics_at_the_1964_Summer_Olympics",1, "M")
result_m <- result_m %>%
rbind.data.frame(result_new)
result_new <- olympic_join("1964", "https://en.wikipedia.org/wiki/Athletics_at_the_1964_Summer_Olympics",2, "F")
result_w <- result_w %>%
rbind.data.frame(result_new)result_new <- olympic_join("1960", "https://en.wikipedia.org/wiki/Athletics_at_the_1960_Summer_Olympics",3, "M")
result_m <- result_m %>%
rbind.data.frame(result_new)
result_new <- olympic_join("1960", "https://en.wikipedia.org/wiki/Athletics_at_the_1960_Summer_Olympics",4, "F")
result_w <- result_w %>%
rbind.data.frame(result_new)result_new <- olympic_join("1956", "https://en.wikipedia.org/wiki/Athletics_at_the_1956_Summer_Olympics",2, "M")
result_m <- result_m %>%
rbind.data.frame(result_new)
result_new <- olympic_join("1956", "https://en.wikipedia.org/wiki/Athletics_at_the_1956_Summer_Olympics",3, "F")
result_w <- result_w %>%
rbind.data.frame(result_new)result_new <- olympic_join("1952", "https://en.wikipedia.org/wiki/Athletics_at_the_1952_Summer_Olympics",2, "M")
result_m <- result_m %>%
rbind.data.frame(result_new)
result_new <- olympic_join("1952", "https://en.wikipedia.org/wiki/Athletics_at_the_1952_Summer_Olympics",3, "F")
result_w <- result_w %>%
rbind.data.frame(result_new)result_new <- olympic_join("1948", "https://en.wikipedia.org/wiki/Athletics_at_the_1948_Summer_Olympics",3, "M")
result_m <- result_m %>%
rbind.data.frame(result_new)
result_new <- olympic_join("1948", "https://en.wikipedia.org/wiki/Athletics_at_the_1948_Summer_Olympics",4, "F")
result_w <- result_w %>%
rbind.data.frame(result_new)result_new <- olympic_join("1936", "https://en.wikipedia.org/wiki/Athletics_at_the_1936_Summer_Olympics",3, "M")
result_m <- result_m %>%
rbind.data.frame(result_new)
head(result_m)## # A tibble: 6 × 18
## ID Name.x Sex Age Height Weight Team NOC Games Year Season City
## <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 13029 Usain S… M 29 196 95 Jama… JAM 2016… 2016 Summer Rio …
## 2 60589 Eliud K… M 31 167 57 Kenya KEN 2016… 2016 Summer Rio …
## 3 103274 David L… M 27 190 76 Kenya KEN 2016… 2016 Summer Rio …
## 4 13029 Usain S… M 25 196 95 Jama… JAM 2012… 2012 Summer Lond…
## 5 60618 Stephen… M 23 172 56 Ugan… UGA 2012… 2012 Summer Lond…
## 6 103274 David L… M 23 190 76 Kenya KEN 2012… 2012 Summer Lond…
## # … with 6 more variables: Sport <chr>, Event <chr>, Medal <chr>, Name.y <chr>,
## # Time <chr>, Rank <chr>result_new <- olympic_join("1936", "https://en.wikipedia.org/wiki/Athletics_at_the_1936_Summer_Olympics",4, "F")
result_w <- result_w %>%
rbind.data.frame(result_new)
head(result_w)## # A tibble: 6 × 18
## ID Name.x Sex Age Height Weight Team NOC Games Year Season City
## <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 108532 Mokgadi… F 25 178 70 Sout… RSA 2016… 2016 Summer Rio …
## 2 116424 Jemima … F 31 160 45 Kenya KEN 2016… 2016 Summer Rio …
## 3 119903 Elaine … F 24 167 57 Jama… JAM 2016… 2016 Summer Rio …
## 4 36870 Shelly-… F 25 160 57 Jama… JAM 2012… 2012 Summer Lond…
## 5 39396 Erba Ti… F 24 165 48 Ethi… ETH 2012… 2012 Summer Lond…
## 6 106272 Mariya … F 26 172 60 Russ… RUS 2012… 2012 Summer Lond…
## # … with 6 more variables: Sport <chr>, Event <chr>, Medal <chr>, Name.y <chr>,
## # Time <chr>, Rank <chr>Select columns from women’s and men’s data frames, eliminating duplicates that were created in the join.
athletics_m_final <- result_m%>%
select(2:7,10,12:14,17,18)
head(athletics_m_final)## # A tibble: 6 × 12
## Name.x Sex Age Height Weight Team Year City Sport Event Time Rank
## <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> <chr> <chr>
## 1 Usain St.… M 29 196 95 Jama… 2016 Rio … Athl… 100 … 9.81 1
## 2 Eliud Kip… M 31 167 57 Kenya 2016 Rio … Athl… Mara… 2:08… 1
## 3 David Lek… M 27 190 76 Kenya 2016 Rio … Athl… 800 … 1:42… 1
## 4 Usain St.… M 25 196 95 Jama… 2012 Lond… Athl… 100 … 9.63… 1
## 5 Stephen K… M 23 172 56 Ugan… 2012 Lond… Athl… Mara… 2:08… 1
## 6 David Lek… M 23 190 76 Kenya 2012 Lond… Athl… 800 … 1:40… 1athletics_w_final <- result_w%>%
select(2:7,10,12:14,17,18)
head(athletics_w_final)## # A tibble: 6 × 12
## Name.x Sex Age Height Weight Team Year City Sport Event Time Rank
## <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> <chr> <chr>
## 1 Mokgadi C… F 25 178 70 Sout… 2016 Rio … Athl… 800 … 1:55… 1
## 2 Jemima Je… F 31 160 45 Kenya 2016 Rio … Athl… Mara… 2:24… 1
## 3 Elaine Th… F 24 167 57 Jama… 2016 Rio … Athl… 100 … 10.71 1
## 4 Shelly-An… F 25 160 57 Jama… 2012 Lond… Athl… 100 … 10.75 1
## 5 Erba Tiki… F 24 165 48 Ethi… 2012 Lond… Athl… Mara… 2:23… 1
## 6 Mariya Se… F 26 172 60 Russ… 2012 Lond… Athl… 800 … <NA> <NA>Remove non-numeric notes from the time fields. Convert hms to decimal seconds where needed for both men and women. Compute BMI = height/weight**2. As professor suggestion, we substituted BMI for weight when exploring correlation. This is the representation Correlation: Performance vs. : BMI.
athletics_m_final <-athletics_m_final %>% na.omit() %>%
mutate(Time = str_extract(Time,"[0-9':''.']+"))
athletics_m_hms <- athletics_m_final %>%
filter(str_detect(athletics_m_final$Time,":"))
athletics_m_hms$Time <- period_to_seconds(hms(athletics_m_hms$Time))
athletics_m_final <- athletics_m_final %>%
filter(str_detect(athletics_m_final$Time,":") == "FALSE") %>%
rbind(athletics_m_hms)%>%
arrange(Event)
head(athletics_m_final)## # A tibble: 6 × 12
## Name.x Sex Age Height Weight Team Year City Sport Event Time Rank
## <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> <chr> <chr>
## 1 Usain St.… M 29 196 95 Jama… 2016 Rio … Athl… 100 … 9.81 1
## 2 Usain St.… M 25 196 95 Jama… 2012 Lond… Athl… 100 … 9.63 1
## 3 Usain St.… M 21 196 95 Jama… 2008 Beij… Athl… 100 … 9.69 1
## 4 Justin Al… M 22 186 80 Unit… 2004 Athi… Athl… 100 … 9.85 1
## 5 Maurice G… M 26 176 80 Unit… 2000 Sydn… Athl… 100 … 9.87 1
## 6 Donovan B… M 28 183 82 Cana… 1996 Atla… Athl… 100 … 9.84 1athletics_w_final <-athletics_w_final %>% na.omit() %>%
mutate(Time = str_extract(Time,"[0-9':''.']+"))
athletics_w_hms <- athletics_w_final %>%
filter(str_detect(athletics_w_final$Time,":"))
athletics_w_hms$Time <- period_to_seconds(hms(athletics_w_hms$Time))
athletics_w_final <- athletics_w_final %>%
filter(str_detect(athletics_w_final$Time,":") == "FALSE") %>%
rbind(athletics_w_hms )%>%
arrange(Event)
head(athletics_w_final)## # A tibble: 6 × 12
## Name.x Sex Age Height Weight Team Year City Sport Event Time Rank
## <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> <chr> <chr>
## 1 Elaine Th… F 24 167 57 Jama… 2016 Rio … Athl… 100 … 10.71 1
## 2 Shelly-An… F 25 160 57 Jama… 2012 Lond… Athl… 100 … 10.75 1
## 3 Yolanda G… F 29 160 55 Unit… 1996 Atla… Athl… 100 … 10.94 1
## 4 Yolanda G… F 25 160 55 Unit… 1992 Barc… Athl… 100 … 10.82 1
## 5 Delorez F… F 28 170 57 Unit… 1988 Seoul Athl… 100 … 10.54 1
## 6 Evelyn As… F 27 165 52 Unit… 1984 Los … Athl… 100 … 10.97 1athletics_m_final$BMI <- as.numeric(athletics_m_final$Weight)/((as.numeric(athletics_m_final$Height)/100)**2)
athletics_w_final$BMI <-
as.numeric(athletics_w_final$Weight)/((as.numeric(athletics_w_final$Height)/100)**2)Perform a multi-variate correlation of BMI with Time for both men and women for each of the three designated Athletics Events(Marathon, 100 hundred metres, and 800 metres) Only 100 meter produced a significant correlation, with the p-value of the estimate (approximately 0.018 for the men). The r_square = 0.2871 was not high enough to conclude that the variation in the speed was solely due to the athlete’s BMI. Clearly, a multi-variate model, including other explanatory variables should be explored.
marathon_m <- athletics_m_final %>%
filter(Event == "Marathon")
marathon_lm_m = lm(as.numeric(marathon_m$Time)~marathon_m$BMI)
summary(marathon_lm_m)##
## Call:
## lm(formula = as.numeric(marathon_m$Time) ~ marathon_m$BMI)
##
## Residuals:
## Min 1Q Median 3Q Max
## -480.8 -358.2 -136.1 284.9 1169.8
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6813.85 2164.09 3.149 0.00621 **
## marathon_m$BMI 65.59 109.41 0.599 0.55728
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 490.5 on 16 degrees of freedom
## Multiple R-squared: 0.02196, Adjusted R-squared: -0.03916
## F-statistic: 0.3593 on 1 and 16 DF, p-value: 0.5573marathon_w <- athletics_w_final %>%
filter(Event == "Marathon")
marathon_lm_w = lm(as.numeric(marathon_w$Time)~marathon_w$BMI)
summary(marathon_lm_w)##
## Call:
## lm(formula = as.numeric(marathon_w$Time) ~ marathon_w$BMI)
##
## Residuals:
## 1 2 3 4 5 6 7
## 45.958 -16.822 -276.171 -4.299 208.549 67.625 -24.841
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6671.41 1012.54 6.589 0.00121 **
## marathon_w$BMI 109.60 53.54 2.047 0.09600 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 159.6 on 5 degrees of freedom
## Multiple R-squared: 0.456, Adjusted R-squared: 0.3471
## F-statistic: 4.19 on 1 and 5 DF, p-value: 0.096hundred_m <- athletics_m_final %>%
filter(Event == "100 metres")
hundred_lm_m = lm(as.numeric(hundred_m$Time)~hundred_m$BMI)
summary(hundred_lm_m)##
## Call:
## lm(formula = as.numeric(hundred_m$Time) ~ hundred_m$BMI)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.33756 -0.15240 -0.00886 0.13275 0.35931
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.96671 0.73988 16.174 9.31e-12 ***
## hundred_m$BMI -0.08084 0.03089 -2.617 0.018 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2086 on 17 degrees of freedom
## Multiple R-squared: 0.2871, Adjusted R-squared: 0.2452
## F-statistic: 6.847 on 1 and 17 DF, p-value: 0.01805hundred_w <- athletics_w_final %>%
filter(Event == "100 metres")
hundred_lm_w = lm(as.numeric(hundred_w$Time)~hundred_w$BMI)
summary(hundred_lm_w)##
## Call:
## lm(formula = as.numeric(hundred_w$Time) ~ hundred_w$BMI)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.55820 -0.21539 -0.06017 0.30644 0.78870
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.79371 1.30320 8.282 9.12e-07 ***
## hundred_w$BMI 0.01544 0.06271 0.246 0.809
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3704 on 14 degrees of freedom
## Multiple R-squared: 0.004311, Adjusted R-squared: -0.06681
## F-statistic: 0.06062 on 1 and 14 DF, p-value: 0.8091eight_hundred_m <- athletics_m_final %>%
filter(Event == "800 metres")
eight_hundred_lm_m = lm(as.numeric(eight_hundred_m$Time)~eight_hundred_m$BMI)
summary(eight_hundred_lm_m)##
## Call:
## lm(formula = as.numeric(eight_hundred_m$Time) ~ eight_hundred_m$BMI)
##
## Residuals:
## Min 1Q Median 3Q Max
## -224.40 -101.87 -19.36 71.54 414.27
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6064.99 547.33 11.081 6.47e-09 ***
## eight_hundred_m$BMI 11.89 25.20 0.472 0.643
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 163.8 on 16 degrees of freedom
## Multiple R-squared: 0.01373, Adjusted R-squared: -0.04791
## F-statistic: 0.2227 on 1 and 16 DF, p-value: 0.6433eight_hundred_w <- athletics_w_final %>%
filter(Event == "800 metres")
eight_hundred_lm_w = lm(as.numeric(eight_hundred_w$Time)~eight_hundred_w$BMI)
summary(eight_hundred_lm_w)##
## Call:
## lm(formula = as.numeric(eight_hundred_w$Time) ~ eight_hundred_w$BMI)
##
## Residuals:
## Min 1Q Median 3Q Max
## -255.54 -120.65 -45.21 97.93 374.22
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6912.428 580.984 11.898 3.17e-07 ***
## eight_hundred_w$BMI 7.934 28.403 0.279 0.786
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 189.4 on 10 degrees of freedom
## Multiple R-squared: 0.007743, Adjusted R-squared: -0.09148
## F-statistic: 0.07803 on 1 and 10 DF, p-value: 0.7857Interestingly, the second order component of weight in the formula for BMI results in an improvement in performance (seconds decline) as BMI increases, up until approximately BMI = 25. Above that value, performance decreases. Perhaps, a certain amount of muscle mass is optimum to achieve the maximum amount of human speed, but above that BMI, it takes too much force to accelerate the mass.
hundred_m_plot<- ggplot(data = hundred_m, aes(BMI, as.numeric(Time))) +xlab("BMI") + ylab("Sec") +
geom_point() +
ggtitle("Men's 100 Yard Dash Performance vs. BMI") +
geom_smooth()
hundred_m_plot## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
The comparison of women’s BMI to 100 yard dash performance does not replicate this trend, as was anticipated by the high p-value for the estimate and low r_square.
##Correlation: Performance
hundred_w_plot<- ggplot(data = hundred_w, aes(BMI, as.numeric(Time))) +xlab("BMI") + ylab("Sec") +
geom_point() +
ggtitle("Women's 100 Yard Dash Performance vs. BMI") +
geom_smooth()
hundred_w_plot
Select columns from Athletics tables for plotting women vs. men’s performance over time.
Olympic_m<- athletics_m_final
Olympic_m$Sex = "M"
Olympic_w<- athletics_w_final
Olympic_w$Sex = "F"
Olympics_Athletics <- Olympic_m %>%
rbind(Olympic_w) %>%
select (9,10,7,2,11,12)
#filter(Rank == "1" )
head(Olympics_Athletics)## # A tibble: 6 × 6
## Sport Event Year Sex Time Rank
## <chr> <chr> <dbl> <chr> <chr> <chr>
## 1 Athletics 100 metres 2016 M 9.81 1
## 2 Athletics 100 metres 2012 M 9.63 1
## 3 Athletics 100 metres 2008 M 9.69 1
## 4 Athletics 100 metres 2004 M 9.85 1
## 5 Athletics 100 metres 2000 M 9.87 1
## 6 Athletics 100 metres 1996 M 9.84 1Plot women’s (green) performance against men’s performance (red) over time. Because of the extreme differences in time scale, a facet wrap did not generate meaningful results.
Women were not allowed to run the Marathon until 1984. An investigation of the sharp increase in time for the women’s Marathon in 1992 was due to a course that finished up a steep incline, sapping their strength at the end of the event.
## Male vs. Female: Track & Field
marathon <- Olympics_Athletics %>%
filter(Event == "Marathon")
marathon$Hours = as.numeric(marathon$Time)/3600
head(marathon)## # A tibble: 6 × 7
## Sport Event Year Sex Time Rank Hours
## <chr> <chr> <dbl> <chr> <chr> <chr> <dbl>
## 1 Athletics Marathon 2016 M 7724 1 2.15
## 2 Athletics Marathon 2012 M 7681 1 2.13
## 3 Athletics Marathon 2008 M 7592 1 2.11
## 4 Athletics Marathon 2000 M 7811 1 2.17
## 5 Athletics Marathon 1996 M 7956 1 2.21
## 6 Athletics Marathon 1992 M 8003 1 2.22Olympic_perf_plot <- ggplot(data = marathon, aes(Year, Hours, colour=Sex)) +
coord_cartesian(ylim=c(2,3)) +
geom_point() +
ggtitle("Marathon") +
geom_smooth()
Olympic_perf_plot
## Male vs. Female: Speed Skating
url <- "https://en.wikipedia.org/wiki/Speed_skating_at_the_Winter_Olympics"
scraplinks <- function(url){
# Create an html document from the url
webpage <- xml2::read_html(url)
# Extract the URLs
url_ <- webpage %>%
rvest::html_nodes("a") %>%
rvest::html_attr("href")
# Extract the link text
link_ <- webpage %>%
rvest::html_nodes("a") %>%
rvest::html_text()
return(tibble(link = link_, url = url_))
}
url_links <- scraplinks(url)
Men_master <- url_links %>% filter(str_detect(url,"_Men")) %>% select(1,2)
Men_master <- Men_master %>% mutate(Year = substr(url,28,31)) %>% select(3,2)
Men_master <- Men_master %>% mutate(url = paste("https://en.wikipedia.org",url,sep=""))
Men_master <- Men_master %>% mutate(event = word(url,-2,sep='_')) %>%
mutate(event = gsub(",","",event)) %>% select(1,3,2)
Men_master <- Men_master %>% filter (!is.na(as.numeric(event)))
#500***********************************************************************************************
Men_master_500 <- Men_master %>% filter(event=="500")
n = count(Men_master_500)$n
rm(rprev)
rm(rank1)
for (i in 1:n) {
year <- Men_master_500$Year[i]
event <- str_c(Men_master_500$event[i],"_meters")
extractf <- Men_master_500$url[i] %>% read_html() %>% html_table(fill = TRUE)
k <- case_when(
(year == "1968" | year == "1984" | year == "1992" ) ~ 5,
(year == "2014") ~ 8,
(year == "2018") ~ 7,
(year == "1932") ~ 9,
TRUE ~ 6
)
sitef <- extractf[[k]]
if (year == "2006") {
sitef <- sitef %>% head(3) %>% select(`Total Time`)
sitef <- rename(sitef, Time = `Total Time`)
}
if (year == "1998" | year == "2002"| year == "2010" | year == "2014") {
sitef <- sitef %>% head(3) %>% select(Total)
sitef <- rename(sitef, Time = Total)
}
rank1 <- sitef %>% head(3) %>% select(Time)
if (year == "1932"){
rank1 <- sitef %>% head(1) %>% select(Time)
}
rank1 <- rank1 %>% mutate(Sport = "Skating", Event = event, Year = year, Gender = "Male" )
rank1 <- rank1 %>% select (2,3,4,5,1) %>% mutate(Time = str_extract(Time,"[0-9':''.']+"))
rank1 <- rank1 %>% mutate(Rank = seq.int(nrow(rank1)))
if(str_detect(rank1$Time,":") == "FALSE"){
rank1$Time <- paste("0.",trimws(rank1$Time),sep="")
}
rank1$Time <- paste("0.",trimws(rank1$Time),sep="")
rank1$Time <- str_replace((rank1$Time),":",".")
rank1 <- rank1 %>% mutate(Timeinsec = period_to_seconds(hms(Time)))
if (i > 1) {
rank1 <- rbind(rprev,rank1)
}
rprev <- rank1
}
r_500 <- rank1 %>% na.omit
#1000**********************************************************************************************
Men_master_1000 <- Men_master %>% filter(event=="1000")
n = count(Men_master_1000)$n
rm(rprev)
rm(rank1)
for (i in 1:n) {
year <- Men_master_1000$Year[i]
event <- str_c(Men_master_1000$event[i],"_meters")
extractf <- Men_master_1000$url[i] %>% read_html() %>% html_table(fill = TRUE)
k <- case_when(
(year == "1984" | year == "1992" | year == "2006" | year == "2010") ~ 5,
TRUE ~ 6
)
sitef <- extractf[[k]]
rank1 <- sitef %>% head(3) %>% select(Time)
rank1 <- rank1 %>% mutate(Sport = "Skating", Event = event, Year = year, Gender = "Male" )
rank1 <- rank1 %>% select (2,3,4,5,1) %>% mutate(Time = str_extract(Time,"[0-9':''.']+"))
rank1 <- rank1 %>% mutate(Rank = seq.int(nrow(rank1)))
if(str_detect(rank1$Time,":") == "FALSE"){
rank1$Time <- paste("0.",trimws(rank1$Time),sep="")
}
rank1$Time <- paste("0.",trimws(rank1$Time),sep="")
rank1$Time <- str_replace((rank1$Time),":",".")
rank1 <- rank1 %>% mutate(Timeinsec = period_to_seconds(hms(Time)))
if (i > 1) {
rank1 <- rbind(rprev,rank1)
}
rprev <- rank1
}
r_1000 <- rank1 %>% na.omit
#1500**********************************************************************************************
Men_master_1500 <- Men_master %>% filter(event=="1500")
n = count(Men_master_1500)$n
rm(rprev)
rm(rank1)
for (i in 1:n) {
year <- Men_master_1500$Year[i]
event <- str_c(Men_master_1500$event[i],"_meters")
extractf <- Men_master_1500$url[i] %>% read_html() %>% html_table(fill = TRUE)
k <- case_when(
(year == "1964" | year == "1984" | year == "1992" | year == "2006" | year == "2010") ~ 5,
(year == "1932") ~ 9,
TRUE ~ 6
)
sitef <- extractf[[k]]
rank1 <- sitef %>% head(3) %>% select(Time)
if (year == "1932"){
rank1 <- sitef %>% head(1) %>% select(Time)
}
rank1 <- rank1 %>% mutate(Sport = "Skating", Event = event, Year = year, Gender = "Male" )
rank1 <- rank1 %>% select (2,3,4,5,1) %>% mutate(Time = str_extract(Time,"[0-9':''.']+"))
rank1 <- rank1 %>% mutate(Rank = seq.int(nrow(rank1)))
if(str_detect(rank1$Time,":") == "FALSE"){
rank1$Time <- paste("0.",trimws(rank1$Time),sep="")
}
rank1$Time <- paste("0.",trimws(rank1$Time),sep="")
rank1$Time <- str_replace((rank1$Time),":",".")
rank1 <- rank1 %>% mutate(Timeinsec = period_to_seconds(hms(Time)))
if (i > 1) {
rank1 <- rbind(rprev,rank1)
}
rprev <- rank1
}
r_1500 <- rank1 %>% na.omit
#5000**********************************************************************************************
Men_master_5000 <- Men_master %>% filter(event=="5000")
n = count(Men_master_5000)$n
rm(rprev)
rm(rank1)
for (i in 1:n) {
year <- Men_master_5000$Year[i]
event <- str_c(Men_master_5000$event[i],"_meters")
extractf <- Men_master_5000$url[i] %>% read_html() %>% html_table(fill = TRUE)
k <- case_when(
(year == "1972" | year == "1976" | year == "1984" | year == "1992" | year == "2006") ~ 5,
(year == "2014") ~ 7,
TRUE ~ 6
)
sitef <- extractf[[k]]
rank1 <- sitef %>% head(3) %>% select(Time)
rank1 <- rank1 %>% mutate(Sport = "Skating", Event = event, Year = year, Gender = "Male" )
rank1 <- rank1 %>% select (2,3,4,5,1) %>% mutate(Time = str_extract(Time,"[0-9':''.']+"))
rank1 <- rank1 %>% mutate(Rank = seq.int(nrow(rank1)))
if(str_detect(rank1$Time,":") == "FALSE"){
rank1$Time <- paste("0.",trimws(rank1$Time),sep="")
}
rank1$Time <- paste("0.",trimws(rank1$Time),sep="")
rank1$Time <- str_replace((rank1$Time),":",".")
rank1 <- rank1 %>% mutate(Timeinsec = period_to_seconds(hms(Time)))
if (i > 1) {
rank1 <- rbind(rprev,rank1)
}
rprev <- rank1
}
r_5000 <- rank1 %>% na.omit
#**************************************************************************************************
rm(Men_extract)
Men_extract <- bind_rows(r_500,r_1000,r_1500,r_5000)
head(Men_extract)## # A tibble: 6 × 7
## Sport Event Year Gender Time Rank Timeinsec
## <chr> <chr> <chr> <chr> <chr> <int> <dbl>
## 1 Skating 500_meters 1924 Male 0.0.44.0 1 44
## 2 Skating 500_meters 1924 Male 0.0.44.2 2 44.2
## 3 Skating 500_meters 1924 Male 0.0.44.8 3 44.8
## 4 Skating 500_meters 1928 Male 0.0.43.4 1 43.4
## 5 Skating 500_meters 1928 Male 0.0.43.4 2 43.4
## 6 Skating 500_meters 1928 Male 0.0.43.6 3 43.6#**************************************************************************************************
Women_master <- url_links %>% filter(str_detect(url,"_Women")) %>% select(1,2)
Women_master <- Women_master %>% mutate(Year = substr(url,28,31)) %>% select(3,2)
Women_master <- Women_master %>% mutate(url = paste("https://en.wikipedia.org",url,sep=""))
Women_master <- Women_master %>% mutate(event = word(url,-2,sep='_')) %>%
mutate(Event = gsub(",","",event)) %>% select(1,3,2)
Women_master <- Women_master %>% filter (!is.na(as.numeric(event)))
#Women 500*****************************************************************************************
Women_master_500 <- Women_master %>% filter(event=="500")
n = count(Women_master_500)$n
rm(rprev)
rm(rank1)
for (i in 1:n) {
year <- Women_master_500$Year[i]
event <- str_c(Women_master_500$event[i],"_meters")
extractf <- Women_master_500$url[i] %>% read_html() %>% html_table(fill = TRUE)
k <- case_when(
(year == "1968"| year == "1992" | year == "1994") ~ 5,
TRUE ~ 6
)
sitef <- extractf[[k]]
if (year == "1998" | year == "2002" | year == "2010") {
#glimpse(sitef)
sitef <- sitef %>% head(3) %>% select(Total)
sitef <- rename(sitef, Time = Total)
}
if (year == "2006") {
#glimpse(sitef)
sitef <- sitef %>% head(3) %>% select(`Total Time`)
sitef <- rename(sitef, Time = `Total Time`)
}
rank1 <- sitef %>% head(3) %>% select(Time)
rank1 <- rank1 %>% mutate(Sport = "Skating", Event = event, Year = year, Gender = "Female" )
rank1 <- rank1 %>% select (2,3,4,5,1) %>% mutate(Time = str_extract(Time,"[0-9':''.']+"))
rank1 <- rank1 %>% mutate(Rank = seq.int(nrow(rank1)))
if(str_detect(rank1$Time,":") == "FALSE"){
rank1$Time <- paste("0.",trimws(rank1$Time),sep="")
}
rank1$Time <- paste("0.",trimws(rank1$Time),sep="")
rank1$Time <- str_replace((rank1$Time),":",".")
rank1 <- rank1 %>% mutate(Timeinsec = period_to_seconds(hms(Time)))
if (i > 1) {
rank1 <- rbind(rprev,rank1)
}
rprev <- rank1
}
r_w_500 <- rank1 %>% na.omit
#Women 1000 ***************************************************************************************
Women_master_1000 <- Women_master %>% filter(event=="1000")
n = count(Women_master_1000)$n
rm(rprev)
rm(rank1)
for (i in 1:n) {
year <- Women_master_1000$Year[i]
event <- str_c(Women_master_1000$event[i],"_meters")
extractf <- Women_master_1000$url[i] %>% read_html() %>% html_table(fill = TRUE)
k <- case_when(
(year == "1992" | year == "1994" | year == "2006" | year == "2010") ~ 5,
TRUE ~ 6
)
sitef <- extractf[[k]]
rank1 <- sitef %>% head(3) %>% select(Time)
rank1 <- rank1 %>% mutate(Sport = "Skating", Event = event, Year = year, Gender = "Female" )
rank1 <- rank1 %>% select (2,3,4,5,1) %>% mutate(Time = str_extract(Time,"[0-9':''.']+"))
rank1 <- rank1 %>% mutate(Rank = seq.int(nrow(rank1)))
if(str_detect(rank1$Time,":") == "FALSE"){
rank1$Time <- paste("0.",trimws(rank1$Time),sep="")
}
rank1$Time <- paste("0.",trimws(rank1$Time),sep="")
rank1$Time <- str_replace((rank1$Time),":",".")
rank1 <- rank1 %>% mutate(Timeinsec = period_to_seconds(hms(Time)))
if (i > 1) {
rank1 <- rbind(rprev,rank1)
}
rprev <- rank1
}
r_w_1000 <- rank1 %>% na.omit
#Women 1500 ***************************************************************************************
Women_master_1500 <- Women_master %>% filter(event=="1500")
n = count(Women_master_1500)$n
rm(rprev)
rm(rank1)
for (i in 1:n) {
year <- Women_master_1500$Year[i]
event <- str_c(Women_master_1500$event[i],"_meters")
extractf <- Women_master_1500$url[i] %>% read_html() %>% html_table(fill = TRUE)
k <- case_when(
(year == "1992" | year == "1994" | year == "2006" | year == "2010" | year == "2018") ~ 5,
TRUE ~ 6
)
sitef <- extractf[[k]]
rank1 <- sitef %>% head(3) %>% select(Time)
rank1 <- rank1 %>% mutate(Sport = "Skating", Event = event, Year = year, Gender = "Female")
rank1 <- rank1 %>% select (2,3,4,5,1) %>% mutate(Time = str_extract(Time,"[0-9':''.']+"))
rank1 <- rank1 %>% mutate(Rank = seq.int(nrow(rank1)))
if(str_detect(rank1$Time,":") == "FALSE"){
rank1$Time <- paste("0.",trimws(rank1$Time),sep="")
}
rank1$Time <- paste("0.",trimws(rank1$Time),sep="")
rank1$Time <- str_replace((rank1$Time),":",".")
rank1 <- rank1 %>% mutate(Timeinsec = period_to_seconds(hms(Time)))
if (i > 1) {
rank1 <- rbind(rprev,rank1)
}
rprev <- rank1
}
r_w_1500 <- rank1 %>% na.omit
#Women 5000 ***************************************************************************************
Women_master_5000 <- Women_master %>% filter(event=="5000")
n = count(Women_master_5000)$n
rm(rprev)
rm(rank1)
for (i in 1:n) {
year <- Women_master_5000$Year[i]
event <- str_c(Women_master_5000$event[i],"_meters")
extractf <- Women_master_5000$url[i] %>% read_html() %>% html_table(fill = TRUE)
k <- case_when(
(year == "1992" | year == "1994" | year == "2006" | year == "2010") ~ 5,
TRUE ~ 6
)
sitef <- extractf[[k]]
rank1 <- sitef %>% head(3) %>% select(Time)
rank1 <- rank1 %>% mutate(Sport = "Skating", Event = event, Year = year, Gender = "Female" )
rank1 <- rank1 %>% select (2,3,4,5,1) %>% mutate(Time = str_extract(Time,"[0-9':''.']+"))
rank1 <- rank1 %>% mutate(Rank = seq.int(nrow(rank1)))
if(str_detect(rank1$Time,":") == "FALSE"){
rank1$Time <- paste("0.",trimws(rank1$Time),sep="")
}
rank1$Time <- paste("0.",trimws(rank1$Time),sep="")
rank1$Time <- str_replace((rank1$Time),":",".")
rank1 <- rank1 %>% mutate(Timeinsec = period_to_seconds(hms(Time)))
if (i > 1) {
rank1 <- rbind(rprev,rank1)
}
rprev <- rank1
}
r_w_5000 <- rank1 %>% na.omit
rm(Women_extract)
Women_extract <- bind_rows(r_w_500,r_w_1000,r_w_1500,r_w_5000)
head(Women_extract)## # A tibble: 6 × 7
## Sport Event Year Gender Time Rank Timeinsec
## <chr> <chr> <chr> <chr> <chr> <int> <dbl>
## 1 Skating 500_meters 1932 Female 0.0.60.4 1 60.4
## 2 Skating 500_meters 1960 Female 0.0.45.9 1 45.9
## 3 Skating 500_meters 1960 Female 0.0.46.0 2 46
## 4 Skating 500_meters 1960 Female 0.0.46.1 3 46.1
## 5 Skating 500_meters 1964 Female 0.0.45 1 45
## 6 Skating 500_meters 1964 Female 0.0.45.4 2 45.4Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.
Combine men and women’s files for plotting.
Olympics_speed_skating <- Men_extract %>%
rbind(Women_extract)
head(Olympics_speed_skating )## # A tibble: 6 × 7
## Sport Event Year Gender Time Rank Timeinsec
## <chr> <chr> <chr> <chr> <chr> <int> <dbl>
## 1 Skating 500_meters 1924 Male 0.0.44.0 1 44
## 2 Skating 500_meters 1924 Male 0.0.44.2 2 44.2
## 3 Skating 500_meters 1924 Male 0.0.44.8 3 44.8
## 4 Skating 500_meters 1928 Male 0.0.43.4 1 43.4
## 5 Skating 500_meters 1928 Male 0.0.43.4 2 43.4
## 6 Skating 500_meters 1928 Male 0.0.43.6 3 43.6plot 500 metres Speed Skating
m500 <- Olympics_speed_skating %>%
filter(Event == "500_meters")
m500$Sec = m500$Timeinsec
Olympic_perf_plot <- ggplot(data = m500, aes(Year, Sec, colour=Gender)) +
coord_cartesian(ylim = c(30,80)) +
geom_point() +
ggtitle("500 metres: Olympic Women Speed Skating Performance vs. Men") +
geom_smooth()
Olympic_perf_plot## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
plot 1000 meter
m1000 <- Olympics_speed_skating %>%
filter(Event == "1000_meters")
m1000$Sec = m1000$Timeinsec
Olympic_perf_plot <- ggplot(data = m1000, aes(Year, Sec, colour=Gender)) +
coord_cartesian(ylim = c(60,130)) +
geom_point() +
ggtitle("1000 metres") +
geom_smooth()
Olympic_perf_plot## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
plot 1500
m1500 <- Olympics_speed_skating %>%
filter(Event == "1500_meters")
m1500$Sec = m1500$Timeinsec
Olympic_perf_plot <- ggplot(data = m1500, aes(Year, Sec, colour=Gender)) +
coord_cartesian(ylim = c(100,200)) +
geom_point() +
ggtitle("1500 metres: Olympic Women Speed Skating Performance vs. Men") +
geom_smooth()
Olympic_perf_plot## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
plot 5000 meters
m5000 <- Olympics_speed_skating %>%
filter(Event == "5000_meters")
m5000$Sec = m5000$Timeinsec
Olympic_perf_plot <- ggplot(data = m5000, aes(Year, Sec, colour=Gender)) +
coord_cartesian(ylim = c(300,600)) +
geom_point() +
ggtitle("5000 metres") +
geom_smooth()
Olympic_perf_plot## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
knitr::opts_chunk$set(echo = TRUE)
url <- "https://en.wikipedia.org/wiki/Swimming_at_the_Summer_Olympics"
scraplinks <- function(url){
# Create an html document from the url
webpage <- xml2::read_html(url)
# Extract the URLs
url_ <- webpage %>%
rvest::html_nodes("a") %>%
rvest::html_attr("href")
# Extract the link text
link_ <- webpage %>%
rvest::html_nodes("a") %>%
rvest::html_text()
return(tibble(link = link_, url = url_))
}
url_links <- scraplinks(url)
Men_master <- url_links %>% filter(str_detect(url,"_Men")) %>% filter(str_detect(link,"X")) %>%
filter(str_detect(url,"_sailors",TRUE)) %>%
filter(str_detect(url,"_yard",TRUE)) %>%
filter(str_detect(url,"_mile",TRUE)) %>%
filter(str_detect(url,"_medley",TRUE)) %>%
filter(str_detect(url,"_relay",TRUE)) %>%
filter(str_detect(url,"_obstacle",TRUE)) %>%
filter(str_detect(url,"_underwater",TRUE)) %>%
filter(str_detect(url,"_marathon",TRUE)) %>%
filter(str_detect(url,"_team",TRUE)) %>% select(1,2)
Men_master <- Men_master %>% mutate(Year = substr(url,23,26)) %>% select(3,2)
Men_master <- Men_master %>% mutate(url = paste("https://en.wikipedia.org",url,sep=""))
Men_master <- Men_master %>% mutate(event = word(url,9,end=11,sep='_')) %>%
mutate(event = gsub(",","",event)) %>% select(1,3,2)
### ************************** 50_metre_freestyle ### **************************
Men_master_50 <- Men_master %>% filter(event=="50_metre_freestyle")
n = count(Men_master_50)$n
rm(rprev)
rm(rank1)
for (i in 1:n) {
year <- Men_master_50$Year[i]
event <- Men_master_50$event[i]
extractf <- Men_master_50$url[i] %>% read_html() %>% html_table(fill = TRUE)
k <- case_when(
(year == "1988" | year == "2020" ) ~ 9,(year == "1992" | year == "1996" | year == "2008" ) ~ 8,
(year == "2000" | year == "2004" | year == "2012" | year == "2016") ~ 7, TRUE ~ 6
)
sitef <- extractf[[k]]
rank1 <- sitef %>% head(3) %>% select(Time)
rank1 <- rank1 %>% mutate(Sport = "Swimming", Event = event, Year = year, Gender = "Male" )
rank1 <- rank1 %>% select (2,3,4,5,1) %>% mutate(Time = str_extract(Time,"[0-9':''.']+"))
rank1 <- rank1 %>% mutate(Rank = seq.int(nrow(rank1)))
if(str_detect(rank1$Time,":") == "FALSE"){
rank1$Time <- paste("0.",trimws(rank1$Time),sep="")
}
rank1$Time <- paste("0.",trimws(rank1$Time),sep="")
rank1$Time <- str_replace((rank1$Time),":",".")
rank1 <- rank1 %>% mutate(Timeinsec = period_to_seconds(hms(Time)))
if (i > 1) {
rank1 <- rbind(rprev,rank1)
}
rprev <- rank1
}
rank50mtre_fs <- rank1 %>% na.omit()
### ************************** 50_metre_freestyle Men ### **************************
### ************************** 100_metre_freestyle Men ### **************************
Men_master_100_fs <- Men_master %>% filter(event=="100_metre_freestyle")
n = count(Men_master_100_fs)$n
rm(rprev)
rm(rank1)
for (i in 1:n) {
year <- Men_master_100_fs$Year[i]
event <- Men_master_100_fs$event[i]
extractf <- Men_master_100_fs$url[i] %>% read_html() %>% html_table(fill = TRUE)
k <- case_when(
(year == "1896" ) ~ 5,
(year == "1960" | year == "1964" | year == "1968" | year == "1980" | year == "1992" | year == "1996" | year == "2000" | year == "2004" | year == "2008" | year == "2012" | year == "2016") ~ 8,
(year == "1952" | year == "1956" | year == "1972" | year == "1976" | year == "1984" | year == "1988" | year == "2020") ~ 9,
(year == "2000" | year == "2004" | year == "2012" | year == "2016") ~ 7,
(year == "1908") ~ 17,
(year == "1912") ~ 20,
(year == "1932") ~ 12,
(year == "1920" | year == "1924" | year == "1948") ~ 14,
(year == "1936") ~ 15,
(year == "1928") ~ 16,
TRUE ~ 6
)
sitef <- extractf[[k]]
if (colnames(sitef)[4] == "Time (1st)"){
colnames(sitef)[4] <- "Time"
}
rank1 <- sitef %>% head(3) %>% select(Time)
rank1 <- rank1 %>% mutate(Sport = "Swimming", Event = event, Year = year, Gender = "Male" )
rank1 <- rank1 %>% select (2,3,4,5,1) %>% mutate(Time = str_extract(Time,"[0-9':''.']+"))
rank1 <- rank1 %>% mutate(Rank = seq.int(nrow(rank1)))
if(str_detect(rank1$Time,":") == "FALSE"){
rank1$Time <- paste("0.",trimws(rank1$Time),sep="")
}
rank1$Time <- paste("0.",trimws(rank1$Time),sep="")
rank1$Time <- str_replace((rank1$Time),":",".")
rank1 <- rank1 %>% mutate(Timeinsec = period_to_seconds(hms(Time)))
if (i > 1) {
rank1 <- rbind(rprev,rank1)
}
rprev <- rank1
}
rank100mtre_fs <- rank1 %>% na.omit()
### ************************** 100_metre_freestyle Men ### **************************
### ************************** 200_metre_freestyle Men ### **************************
Men_master_200_fs <- Men_master %>% filter(event=="200_metre_freestyle")
n = count(Men_master_200_fs)$n
rm(rprev)
rm(rank1)
for (i in 1:n) {
year <- Men_master_200_fs$Year[i]
event <- Men_master_200_fs$event[i]
extractf <- Men_master_200_fs$url[i] %>% read_html() %>% html_table(fill = TRUE)
k <- case_when(
(year == "1900" ) ~ 10,
(year == "1984" | year == "1988" | year == "1992" | year == "1996"
| year == "2000" | year == "2004" | year == "2008" | year == "2020") ~ 9,
(year == "1968" | year == "1972" | year == "1976" ) ~ 7,
(year == "1980" | year == "2012" | year == "2016") ~ 8,
TRUE ~ 6
)
sitef <- extractf[[k]]
rank1 <- sitef %>% head(3) %>% select(Time)
rank1 <- rank1 %>% mutate(Sport = "Swimming", Event = event, Year = year, Gender = "Male" )
rank1 <- rank1 %>% select (2,3,4,5,1) %>% mutate(Time = str_extract(Time,"[0-9':''.']+"))
rank1 <- rank1 %>% mutate(Rank = seq.int(nrow(rank1)))
if(str_detect(rank1$Time,":") == "FALSE"){
rank1$Time <- paste("0.",trimws(rank1$Time),sep="")
}
rank1$Time <- paste("0.",trimws(rank1$Time),sep="")
rank1$Time <- str_replace((rank1$Time),":",".")
rank1 <- rank1 %>% mutate(Timeinsec = period_to_seconds(hms(Time)))
if (i > 9) {
rank1 <- rbind(rprev,rank1)
}
rprev <- rank1
}
rank200mtre_fs <- rank1 %>% na.omit()
### ************************** 200_metre_freestyle Men ### **************************
### ************************** 400_metre_freestyle Men ### **************************
Men_master_400_fs <- Men_master %>% filter(event=="400_metre_freestyle")
n = count(Men_master_400_fs)$n
rm(rprev)
rm(rank1)
k=0
for (i in 1:n) {
year <- Men_master_400_fs$Year[i]
event <- Men_master_400_fs$event[i]
extractf <- Men_master_400_fs$url[i] %>% read_html() %>% html_table(fill = TRUE)
k <- case_when(
(year == "1900" | year == "1964") ~ 10, (year == "1948") ~5, ( year == "2004" | year == "2008" | year == "2016" ) ~6, (year == "1912" | year == "1928") ~13, (year == "1920" | year == "1924" | year == "1936") ~12, (year == "1968" | year == "1972" | year == "1980" | year =="1996" | year == "2000"| year == "2020" |year == "2012") ~ 7,(year == "1956" | year == "1960") ~9,
( year == "1984" | year == "1988" | year == "1992") ~ 8, (year == "1956" | year == "1976" | year == "1932"| year == "1952") ~11, (year == "1908") ~16,
TRUE ~ 15
)
sitef <- extractf[[k]]
rank1 <- sitef %>% head(3) %>% select(Time)
rank1 <- rank1 %>% mutate(Sport = "Swimming", Event = event, Year = year, Gender = "Male" )
rank1 <- rank1 %>% select (2,3,4,5,1) %>% mutate(Time = str_extract(Time,"[0-9':''.']+"))
rank1 <- rank1 %>% mutate(Rank = seq.int(nrow(rank1)))
if(str_detect(rank1$Time,":") == "FALSE"){
rank1$Time <- paste("0.",trimws(rank1$Time),sep="")
}
rank1$Time <- paste("0.",trimws(rank1$Time),sep="")
rank1$Time <- str_replace((rank1$Time),":",".")
rank1 <- rank1 %>% mutate(Timeinsec = period_to_seconds(hms(Time)))
if (i > 1) {
rank1 <- rbind(rprev,rank1)
}
rprev <- rank1
}
rank400mtre_fs <- rank1 %>% na.omit()
### ************************** 400_metre_freestyle Men ### **************************
### ************************** 100_metre_backstroke Men ### **************************
Men_master_100_bckstroke <- Men_master %>% filter(event=="100_metre_backstroke")
n = count(Men_master_100_bckstroke)$n
rm(rprev)
rm(rank1)
for (i in 1:n) {
year <- Men_master_100_bckstroke$Year[i]
event <- Men_master_100_bckstroke$event[i]
extractf <- Men_master_100_bckstroke$url[i] %>% read_html() %>% html_table(fill = TRUE)
k <- case_when(
(year == "1980" | year=="1984"| year == "1996" | year =="2004" | year == "1920") ~7, (year == "1908") ~14,
(year == "1988" | year =="1992"| year == "2020") ~ 8,
(year == "1912" | year == "1924" | year == "1928" | year == "1968") ~ 12,
(year == "1932" | year == "1952" | year == "1960" | year =="1972" | year == "1976") ~ 10,
(year == "1936" | year == "1948" ) ~ 11,
(year == "1956" | year == "2000"|year =="2008" | year == "2012"|year == "2016") ~9,
TRUE ~ 6
)
sitef <- extractf[[k]]
rank1 <- sitef %>% head(3) %>% select(Time)
rank1 <- rank1 %>% mutate(Sport = "Swimming", Event = event, Year = year, Gender = "Male" )
rank1 <- rank1 %>% select (2,3,4,5,1) %>% mutate(Time = str_extract(Time,"[0-9':''.']+"))
rank1 <- rank1 %>% mutate(Rank = seq.int(nrow(rank1)))
if(str_detect(rank1$Time,":") == "FALSE"){
rank1$Time <- paste("0.",trimws(rank1$Time),sep="")
}
rank1$Time <- paste("0.",trimws(rank1$Time),sep="")
rank1$Time <- str_replace((rank1$Time),":",".")
rank1 <- rank1 %>% mutate(Timeinsec = period_to_seconds(hms(Time)))
if (i > 9) {
rank1 <- rbind(rprev,rank1)
}
rprev <- rank1
}
rank100mtre_backstroke <- rank1 %>% na.omit()
### ************************** 100_metre_backstroke Men Ends ### **************************
### ************************** 100_metre_breaststroke Men ### **************************
Men_master_100_breaststroke<- Men_master %>% filter(event=="100_metre_breaststroke")
n = count(Men_master_100_breaststroke)$n
rm(rprev)
rm(rank1)
for (i in 1:n) {
year <- Men_master_100_breaststroke$Year[i]
event <- Men_master_100_breaststroke$event[i]
extractf <- Men_master_100_breaststroke$url[i] %>% read_html() %>% html_table(fill = TRUE)
k <- case_when(
(year == "1980") ~ 6,
(year == "1984" | year =="1988"| year == "1992" | year =="1996" | year == "2020") ~ 8,
(year == "1972") ~ 10,
(year == "1968") ~ 11,
(year == "1976" | year == "2000"|year =="2004" | year == "2008"|year == "2012" | year == "2016") ~9,
TRUE ~ 6
)
sitef <- extractf[[k]]
rank1 <- sitef %>% head(3) %>% select(Time)
rank1 <- rank1 %>% mutate(Sport = "Swimming", Event = event, Year = year, Gender = "Male" )
rank1 <- rank1 %>% select (2,3,4,5,1) %>% mutate(Time = str_extract(Time,"[0-9':''.']+"))
rank1 <- rank1 %>% mutate(Rank = seq.int(nrow(rank1)))
if(str_detect(rank1$Time,":") == "FALSE"){
rank1$Time <- paste("0.",trimws(rank1$Time),sep="")
}
rank1$Time <- paste("0.",trimws(rank1$Time),sep="")
rank1$Time <- str_replace((rank1$Time),":",".")
rank1 <- rank1 %>% mutate(Timeinsec = period_to_seconds(hms(Time)))
if (i > 1) {
rank1 <- rbind(rprev,rank1)
}
rprev <- rank1
}
rank100mtre_breaststroke <- rank1 %>% na.omit
### ************************** 100metre_breaststroke Men Ends ### **************************
### ************************** 100_metre_butterfly Men ### **************************
Men_master_100_metre_butterfly<- Men_master %>% filter(event=="100_metre_butterfly")
n = count(Men_master_100_metre_butterfly)$n
rm(rprev)
rm(rank1)
for (i in 1:n) {
year <- Men_master_100_metre_butterfly$Year[i]
event <- Men_master_100_metre_butterfly$event[i]
extractf <- Men_master_100_metre_butterfly$url[i] %>% read_html() %>% html_table(fill = TRUE)
k <- case_when(
(year == "1980" | year == "1992") ~7,(year == "1984" | year =="1988"| year == "1996" | year =="2012" ) ~ 8,
(year == "1972" | year =="1976") ~ 10,(year == "1968") ~ 14,
(year == "2000" | year == "2004"|year =="2008" | year == "2016"|year == "2020" ) ~9,
TRUE ~ 6
)
sitef <- extractf[[k]]
rank1 <- sitef %>% head(3) %>% select(Time)
rank1 <- rank1 %>% mutate(Sport = "Swimming", Event = event, Year = year, Gender = "Male" )
rank1 <- rank1 %>% select (2,3,4,5,1) %>% mutate(Time = str_extract(Time,"[0-9':''.']+"))
rank1 <- rank1 %>% mutate(Rank = seq.int(nrow(rank1)))
if(str_detect(rank1$Time,":") == "FALSE"){
rank1$Time <- paste("0.",trimws(rank1$Time),sep="")
}
rank1$Time <- paste("0.",trimws(rank1$Time),sep="")
rank1$Time <- str_replace((rank1$Time),":",".")
rank1 <- rank1 %>% mutate(Timeinsec = period_to_seconds(hms(Time)))
if (i > 9) {
rank1 <- rbind(rprev,rank1)
}
rprev <- rank1
}
rank100mtre_butterfly <- rank1 %>% na.omit
### ************************** 100_metre_butterfly Men Ends ### **************************
rm(Final_Men_RankingByTime)
Final_Men_RankingByTime <- bind_rows(rank50mtre_fs,
rank100mtre_fs,
rank200mtre_fs,
rank400mtre_fs,
rank100mtre_backstroke,
rank100mtre_breaststroke,
rank100mtre_butterfly)
head(Final_Men_RankingByTime)## # A tibble: 6 × 7
## Sport Event Year Gender Time Rank Timeinsec
## <chr> <chr> <chr> <chr> <chr> <int> <dbl>
## 1 Swimming 50_metre_freestyle 1988 Male 0.0.22.14 1 22.1
## 2 Swimming 50_metre_freestyle 1988 Male 0.0.22.36 2 22.4
## 3 Swimming 50_metre_freestyle 1988 Male 0.0.22.71 3 22.7
## 4 Swimming 50_metre_freestyle 1992 Male 0.0.21.91 1 21.9
## 5 Swimming 50_metre_freestyle 1992 Male 0.0.22.09 2 22.1
## 6 Swimming 50_metre_freestyle 1992 Male 0.0.22.3 3 22.3url <- "https://en.wikipedia.org/wiki/Swimming_at_the_Summer_Olympics"
scraplinks <- function(url){
# Create an html document from the url
webpage <- xml2::read_html(url)
# Extract the URLs
url_ <- webpage %>%
rvest::html_nodes("a") %>%
rvest::html_attr("href")
link_ <- webpage %>%
rvest::html_nodes("a") %>%
rvest::html_text()
return(tibble(link = link_, url = url_))
# Extract the link text
}
url_links <- scraplinks(url)
Women_master <- url_links %>% filter(str_detect(url,"_Women")) %>% filter(str_detect(link,"X")) %>%
filter(str_detect(url,"_sailors",TRUE)) %>%
filter(str_detect(url,"_yard",TRUE)) %>%
filter(str_detect(url,"_mile",TRUE)) %>%
filter(str_detect(url,"_medley",TRUE)) %>%
filter(str_detect(url,"_relay",TRUE)) %>%
filter(str_detect(url,"_obstacle",TRUE)) %>%
filter(str_detect(url,"_underwater",TRUE)) %>%
filter(str_detect(url,"_marathon",TRUE)) %>%
filter(str_detect(url,"_team",TRUE)) %>% select(1,2)
Women_master <- Women_master %>% mutate(Year = substr(url,23,26)) %>% select(3,2)
Women_master <- Women_master %>% mutate(url = paste("https://en.wikipedia.org",url,sep=""))
Women_master <- Women_master %>% mutate(event = word(url,9,end=11,sep='_')) %>%
mutate(event = gsub(",","",event)) %>% select(1,3,2)
### ************************** 50_metre_freestyle Women ### **************************
Women_master_50<- Women_master %>% filter(event=="50_metre_freestyle")
n = count(Women_master_50)$n
rm(rprev)
rm(rank1)
for (i in 1:n) {
year <- Women_master_50$Year[i]
event <- Women_master_50$event[i]
extractf <- Women_master_50$url[i] %>% read_html() %>% html_table(fill = TRUE)
k <- case_when(
(year == "2000") ~ 10, (year == "2008" | year == "2020") ~9, (year == "2012") ~10,
(year == "1988" | year == "1992" | year == "2004" | year == "2016") ~ 8,(year == "1996") ~ 7,
TRUE ~ 6
)
sitef <- extractf[[k]]
rank1 <- sitef %>% head(3) %>% select(Time)
rank1 <- rank1 %>% mutate(Sport = "Swimming", Event = event, Year = year, Gender = "Female" )
rank1 <- rank1 %>% select (2,3,4,5,1) %>% mutate(Time = str_extract(Time,"[0-9':''.']+"))
rank1 <- rank1 %>% mutate(Rank = seq.int(nrow(rank1)))
if(str_detect(rank1$Time,":") == "FALSE"){
rank1$Time <- paste("0.",trimws(rank1$Time),sep="")
}
rank1$Time <- paste("0.",trimws(rank1$Time),sep="")
rank1$Time <- str_replace((rank1$Time),":",".")
rank1 <- rank1 %>% mutate(Timeinsec = period_to_seconds(hms(Time)))
if (i > 1) {
rank1 <- rbind(rprev,rank1)
}
rprev <- rank1
}
rank50mtre_fs_wn <- rank1 %>% na.omit()
### ************************** 50_metre_freestyle Women Ends ### **************************
### ************************** 100_metre_freestyle Women ### **************************
Women_master_100_fs <- Women_master %>% filter(event=="100_metre_freestyle")
n = count(Women_master_100_fs)$n
rm(rprev)
rm(rank1)
for (i in 1:n) {
year <- Women_master_100_fs$Year[i]
event <- Women_master_100_fs$event[i]
extractf <- Women_master_100_fs$url[i] %>% read_html() %>% html_table(fill = TRUE)
k <- case_when(
(year == "1948" ) ~ 6,
(year == "1920" | year == "1992" | year == "1996") ~ 8,
(year == "1956" | year == "2000" | year == "2004" | year == "2008" | year == "2012" | year == "2016" ) ~ 9,
(year == "1960" | year == "1980" | year == "1984" | year == "1988") ~ 7,
(year == "2020") ~ 10, (year == "1936") ~ 12, (year == "1968") ~ 14,
(year == "1924" | year =="1932" | year == "1952" | year == "1964" | year == "1972") ~ 11,
(year == "1976") ~ 13,
TRUE ~ 6
)
sitef <- extractf[[k]]
rank1 <- sitef %>% head(3) %>% select(Time)
rank1 <- rank1 %>% mutate(Sport = "Swimming", Event = event, Year = year, Gender = "Female" )
rank1 <- rank1 %>% select (2,3,4,5,1) %>% mutate(Time = str_extract(Time,"[0-9':''.']+"))
rank1 <- rank1 %>% mutate(Rank = seq.int(nrow(rank1)))
if(str_detect(rank1$Time,":") == "FALSE"){
rank1$Time <- paste("0.",trimws(rank1$Time),sep="")
}
rank1$Time <- paste("0.",trimws(rank1$Time),sep="")
rank1$Time <- str_replace((rank1$Time),":",".")
rank1 <- rank1 %>% mutate(Timeinsec = period_to_seconds(hms(Time)))
if (i > 1) {
rank1 <- rbind(rprev,rank1)
}
rprev <- rank1
}
rank100mtre_fs_wn <- rank1%>% na.omit()
## ****************************** 100_meter_freestyle Woment ends**********************
## ****************************** 200_meter_freestyle Woment **********************
Women_master_200_fs <- Women_master %>% filter(event=="200_metre_freestyle")
n = count(Women_master_200_fs)$n
rm(rprev)
rm(rank1)
for (i in 1:n) {
year <- Women_master_200_fs$Year[i]
event <- Women_master_200_fs$event[i]
extractf <- Women_master_200_fs$url[i] %>% read_html() %>% html_table(fill = TRUE)
k <- case_when(
(year == "1968" ) ~ 10,
(year == "1976" | year == "2008" | year == "2012") ~ 9,
(year == "1980" | year == "1984" | year == "1992" | year == "1996"| year == "2000") ~ 7,
(year == "1972" | year == "1988" | year == "2004" | year == "2016" | year =="2020") ~ 8,
TRUE ~ 6
)
sitef <- extractf[[k]]
rank1 <- sitef %>% head(3) %>% select(Time)
rank1 <- rank1 %>% mutate(Sport = "Swimming", Event = event, Year = year, Gender = "Female" )
rank1 <- rank1 %>% select (2,3,4,5,1) %>% mutate(Time = str_extract(Time,"[0-9':''.']+"))
rank1 <- rank1 %>% mutate(Rank = seq.int(nrow(rank1)))
if(str_detect(rank1$Time,":") == "FALSE"){
rank1$Time <- paste("0.",trimws(rank1$Time),sep="")
}
rank1$Time <- paste("0.",trimws(rank1$Time),sep="")
rank1$Time <- str_replace((rank1$Time),":",".")
rank1 <- rank1 %>% mutate(Timeinsec = period_to_seconds(hms(Time)))
if (i > 1) {
rank1 <- rbind(rprev,rank1)
}
rprev <- rank1
}
rank200mtre_fs_wn <- rank1%>% na.omit()
## ****************************** 200_meter_freestyle Women ends **********************
## ****************************** 400_meter_freestyle Women ends **********************
Women_master_400_fs <- Women_master %>% filter(event=="400_metre_freestyle")
n = count(Women_master_400_fs)$n
rm(rprev)
rm(rank1)
for (i in 1:n) {
year <- Women_master_400_fs$Year[i]
event <- Women_master_400_fs$event[i]
extractf <- Women_master_400_fs$url[i] %>% read_html() %>% html_table(fill = TRUE)
k <- case_when(
(year == "1932" | year =="1952") ~ 10, (year == "1924" | year =="1928" | year =="1936") ~11, (year == "1948") ~5,
(year == "1968" | year == "1976" | year == "" | year == "" | year == "") ~ 9,
(year == "1972" | year == "1980" | year == "1992" | year == "1996"
| year == "2008" | year == "2012" | year == "2016" | year == "2020") ~ 7,
(year == "1956" | year == "1964" | year == "1984" | year =="1988") ~ 8,
( year == "1960" | year =="2000" | year == "2004") ~6,
TRUE ~ 6
)
sitef <- extractf[[k]]
rank1 <- sitef %>% head(3) %>% select(Time)
rank1 <- rank1 %>% mutate(Sport = "Swimming", Event = event, Year = year, Gender = "Female" )
rank1 <- rank1 %>% select (2,3,4,5,1) %>% mutate(Time = str_extract(Time,"[0-9':''.']+"))
rank1 <- rank1 %>% mutate(Rank = seq.int(nrow(rank1)))
if(str_detect(rank1$Time,":") == "FALSE"){
rank1$Time <- paste("0.",trimws(rank1$Time),sep="")
}
rank1$Time <- paste("0.",trimws(rank1$Time),sep="")
rank1$Time <- str_replace((rank1$Time),":",".")
rank1 <- rank1 %>% mutate(Timeinsec = period_to_seconds(hms(Time)))
if (i > 1) {
rank1 <- rbind(rprev,rank1)
}
rprev <- rank1
}
rank400mtre_fs_wn <- rank1%>% na.omit()
## ****************************** 400_meter_freestyle Women ends **********************
## ****************************** 100_metre_backstroke Women **********************
Women_master_100_bckstroke <- Women_master %>% filter(event=="100_metre_backstroke")
n = count(Women_master_100_bckstroke)$n
rm(rprev)
rm(rank1)
for (i in 1:n) {
year <- Women_master_100_bckstroke$Year[i]
event <- Women_master_100_bckstroke$event[i]
extractf <- Women_master_100_bckstroke$url[i] %>% read_html() %>% html_table(fill = TRUE)
k <- case_when(
(year == "1924" | year == "1932" | year == "1956" | year =="1960" | year == "1964" | year == "1980" | year =="1996") ~7,
(year == "1928" | year =="1984"| year == "1992" | year =="2016") ~ 8,
(year == "1968") ~ 14, (year =="1948") ~5, (year == "1952") ~6,
(year == "1936" | year == "1976"| year =="1988" | year == "2000"|year == "2004"
| year =="2008" | year == "1972" | year == "2012" | year == "2020") ~9,
TRUE ~ 21
)
sitef <- extractf[[k]]
rank1 <- sitef %>% head(3) %>% select(Time)
rank1 <- rank1 %>% mutate(Sport = "Swimming", Event = event, Year = year, Gender = "Female" )
rank1 <- rank1 %>% select (2,3,4,5,1) %>% mutate(Time = str_extract(Time,"[0-9':''.']+"))
rank1 <- rank1 %>% mutate(Rank = seq.int(nrow(rank1)))
if(str_detect(rank1$Time,":") == "FALSE"){
rank1$Time <- paste("0.",trimws(rank1$Time),sep="")
}
rank1$Time <- paste("0.",trimws(rank1$Time),sep="")
rank1$Time <- str_replace((rank1$Time),":",".")
rank1 <- rank1 %>% mutate(Timeinsec = period_to_seconds(hms(Time)))
if (i > 1) {
rank1 <- rbind(rprev,rank1)
}
rprev <- rank1
}
rank100mtrebackstroke_fs_wn <- rank1%>% na.omit()
## ****************************** 100_metre_backstroke Women ends **********************
## ****************************** 100_metre_breaststroke Women **********************
Women_master_100_breaststroke<- Women_master %>% filter(event=="100_metre_breaststroke")
n = count(Women_master_100_breaststroke)$n
rm(rprev)
rm(rank1)
for (i in 1:n) {
year <- Women_master_100_breaststroke$Year[i]
event <- Women_master_100_breaststroke$event[i]
extractf <- Women_master_100_breaststroke$url[i] %>% read_html() %>% html_table(fill = TRUE)
k <- case_when(
(year == "1992" | year == "1980") ~ 7,
(year == "1984" | year =="1988"| year == "1996" | year =="2000") ~ 8,
(year == "1968" | year == "1976") ~ 10,
(year == "1972"|year =="2004" | year == "2008"|year == "2012" | year == "2016"|year == "2020") ~9,
TRUE ~ 5
)
sitef <- extractf[[k]]
rank1 <- sitef %>% head(3) %>% select(Time)
rank1 <- rank1 %>% mutate(Sport = "Swimming", Event = event, Year = year, Gender = "Female" )
rank1 <- rank1 %>% select (2,3,4,5,1) %>% mutate(Time = str_extract(Time,"[0-9':''.']+"))
rank1 <- rank1 %>% mutate(Rank = seq.int(nrow(rank1)))
if(str_detect(rank1$Time,":") == "FALSE"){
rank1$Time <- paste("0.",trimws(rank1$Time),sep="")
}
rank1$Time <- paste("0.",trimws(rank1$Time),sep="")
rank1$Time <- str_replace((rank1$Time),":",".")
rank1 <- rank1 %>% mutate(Timeinsec = period_to_seconds(hms(Time)))
if(str_detect(rank1$Time,":") == "FALSE"){
rank1$Time <- paste("0.",trimws(rank1$Time),sep="")
}
rank1$Time <- paste("0.",trimws(rank1$Time),sep="")
rank1$Time <- str_replace((rank1$Time),":",".")
rank1 <- rank1 %>% mutate(Timeinsec = period_to_seconds(hms(Time)))
if (i > 1) {
rank1 <- rbind(rprev,rank1)
}
rprev <- rank1
}
rank100mtrebreaststroke_fs_wn <- rank1%>% na.omit()
## ****************************** 100_metre_breaststroke Women ends **********************
## ****************************** 100_metre_butterfly Women ends **********************
Women_master_100_metre_butterfly<- Women_master %>% filter(event=="100_metre_butterfly")
n = count(Women_master_100_metre_butterfly)$n
rm(rprev)
rm(rank1)
for (i in 1:n) {
year <- Women_master_100_metre_butterfly$Year[i]
event <- Women_master_100_metre_butterfly$event[i]
extractf <- Women_master_100_metre_butterfly$url[i] %>% read_html() %>% html_table(fill = TRUE)
k <- case_when(
(year == "1956" | year == "1980") ~6, (year == "1968") ~ 11, (year == "1960" | year =="1996") ~ 7,
(year == "1964") ~ 12, (year == "1976") ~10, (year == "1984" | year == "1988"
| year == "1992" | year == "2004" | year == "2008" | year == "2020") ~8,
(year == "1972" | year == "2000"|year =="2012" | year == "2016"|year == " " ) ~9,
TRUE ~ 11
)
sitef <- extractf[[k]]
rank1 <- sitef %>% head(3) %>% select(Time)
rank1 <- rank1 %>% mutate(Sport = "Swimming", Event = event, Year = year, Gender = "Female" )
rank1 <- rank1 %>% select (2,3,4,5,1) %>% mutate(Time = str_extract(Time,"[0-9':''.']+"))
rank1 <- rank1 %>% mutate(Rank = seq.int(nrow(rank1)))
if(str_detect(rank1$Time,":") == "FALSE"){
rank1$Time <- paste("0.",trimws(rank1$Time),sep="")
}
rank1$Time <- paste("0.",trimws(rank1$Time),sep="")
rank1$Time <- str_replace((rank1$Time),":",".")
rank1 <- rank1 %>% mutate(Timeinsec = period_to_seconds(hms(Time)))
if (i > 1) {
rank1 <- rbind(rprev,rank1)
}
rprev <- rank1
}
rank100mtrebutterfly_fs_wn <- rank1%>% na.omit()
## ****************************** 100_metre_ butterfly Women ends **********************
rm(Final_Women_RankingByTime)
Final_Women_RankingByTime <-bind_rows(rank50mtre_fs_wn,
rank100mtre_fs_wn,
rank200mtre_fs_wn,
rank400mtre_fs_wn,
rank100mtrebackstroke_fs_wn,
rank100mtrebreaststroke_fs_wn,
rank100mtrebutterfly_fs_wn)
head(Final_Women_RankingByTime)## # A tibble: 6 × 7
## Sport Event Year Gender Time Rank Timeinsec
## <chr> <chr> <chr> <chr> <chr> <int> <dbl>
## 1 Swimming 50_metre_freestyle 1988 Female 0.0.25.49 1 25.5
## 2 Swimming 50_metre_freestyle 1988 Female 0.0.25.64 2 25.6
## 3 Swimming 50_metre_freestyle 1988 Female 0.0.25.71 3 25.7
## 4 Swimming 50_metre_freestyle 1992 Female 0.0.24.79 1 24.8
## 5 Swimming 50_metre_freestyle 1992 Female 0.0.25.08 2 25.1
## 6 Swimming 50_metre_freestyle 1992 Female 0.0.25.23 3 25.2Combine women’s and men’s swimming files for plotting
Olympics_swimming <- Final_Men_RankingByTime %>%
rbind(Final_Women_RankingByTime)
head(Olympics_swimming)## # A tibble: 6 × 7
## Sport Event Year Gender Time Rank Timeinsec
## <chr> <chr> <chr> <chr> <chr> <int> <dbl>
## 1 Swimming 50_metre_freestyle 1988 Male 0.0.22.14 1 22.1
## 2 Swimming 50_metre_freestyle 1988 Male 0.0.22.36 2 22.4
## 3 Swimming 50_metre_freestyle 1988 Male 0.0.22.71 3 22.7
## 4 Swimming 50_metre_freestyle 1992 Male 0.0.21.91 1 21.9
## 5 Swimming 50_metre_freestyle 1992 Male 0.0.22.09 2 22.1
## 6 Swimming 50_metre_freestyle 1992 Male 0.0.22.3 3 22.3plot 100 metre freestyle
m100fs <- Olympics_swimming %>%
filter(Event == "100_metre_freestyle")
m100fs$Sec = m100fs$Timeinsec
Olympic_perf_plot <- ggplot(data = m100fs, aes(Year, Sec, colour=Gender)) +
coord_cartesian(ylim = c(30,70)) +
geom_point() +
ggtitle("100 metres Freestyle: Olympic Women Swimming Performance vs. Men") +
geom_smooth()
Olympic_perf_plot## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
plot 100 metre Butterfly
m100bf <- Olympics_swimming %>%
filter(Event == "100_metre_butterfly")
m100bf$Sec = m100bf$Timeinsec
Olympic_perf_plot <- ggplot(data = m100bf, aes(Year, Sec, colour=Gender)) +
coord_cartesian(ylim = c(30,70)) +
geom_point() +
ggtitle("100 metres Butterfly: Olympic Women Swimming Performance vs. Men") +
geom_smooth()
Olympic_perf_plot## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
plot 400 metre Freestyle
m400fs <- Olympics_swimming %>%
filter(Event == "400_metre_freestyle")
m400fs$Sec = m400fs$Timeinsec
Olympic_perf_plot <- ggplot(data = m400fs, aes(Year, Sec, colour=Gender)) +
coord_cartesian(ylim = c(30,500)) +
geom_point() +
ggtitle("400 metres Freestyle: Olympic Women Swimming Performance vs. Men") +
geom_smooth()
Olympic_perf_plot## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
plot 100 metre Backstroke
m100bs <- Olympics_swimming %>%
filter(Event == "100_metre_backstroke")
m100bs$Sec = m100bs$Timeinsec
Olympic_perf_plot <- ggplot(data = m100bs, aes(Year, Sec, colour=Gender)) +
coord_cartesian(ylim = c(30,100)) +
geom_point() +
ggtitle("100 metres Backstroke") +
geom_smooth()
Olympic_perf_plot## `geom_smooth()` using method = 'loess' and formula 'y ~ x'